(2024) 22:336
Erlich et al. BMC Medicine
https://doi.org/10.1186/s12916-024-03524-7

BMC Medicine

RESEARCH ARTICLE

Open Access

A systematic review and meta‑analysisof randomized trials of substituting soymilk
for cow’s milk and intermediate cardiometabolic
outcomes: understanding the impact of dairy
alternatives in the transition to plant‑based
diets on cardiometabolic health
M. N. Erlich1,2, D. Ghidanac1,2, S. Blanco Mejia1,2, T. A. Khan1,2, L. Chiavaroli1,2,3, A. Zurbau1,2, S. Ayoub‑Charette1,2,
A. Almneni4, M. Messina5, L. A. Leiter1,2,3,6,7, R. P. Bazinet1, D. J. A. Jenkins1,2,3,6,7, C. W. C. Kendall1,2,8 and
J. L. Sievenpiper1,2,3,6,7*

Abstract
Background Dietary guidelines recommend a shift to plant-based diets. Fortified soymilk, a prototypical plant
protein food used in the transition to plant-based diets, usually contains added sugars to match the sweetnessof cow’s milk and is classified as an ultra-processed food. Whether soymilk can replace minimally processed cow’s milk
without the adverse cardiometabolic effects attributed to added sugars and ultra-processed foods remains unclear.
We conducted a systematic review and meta-analysis of randomized controlled trials, to assess the effect of substitut‑ing soymilk for cow’s milk and its modification by added sugars (sweetened versus unsweetened) on intermediate
cardiometabolic outcomes.
Methods MEDLINE, Embase, and the Cochrane Central Register of Controlled Trials were searched (through June2024) for randomized controlled trials of ≥ 3 weeks in adults. Outcomes included established markers of blood
lipids, glycemic control, blood pressure, inflammation, adiposity, renal disease, uric acid, and non-alcoholic fatty liverdisease. Two independent reviewers extracted data and assessed risk of bias. The certainty of evidence was assessedusing GRADE (Grading of Recommendations, Assessment, Development, and Evaluation). A sub-study of lactose
versus sucrose outside of a dairy-like matrix was conducted to explore the role of sweetened soymilk which followed
the same methodology.
Results Eligibility criteria were met by 17 trials (n = 504 adults with a range of health statuses), assessing the effectof a median daily dose of 500 mL of soymilk (22 g soy protein and 17.2 g or 6.9 g/250 mL added sugars) in substitu‑
tion for 500 mL of cow’s milk (24 g milk protein and 24 g or 12 g/250 mL total sugars as lactose) on 19 intermedi‑
ate outcomes. The substitution of soymilk for cow’s milk resulted in moderate reductions in non-HDL-C (mean*Correspondence:
J. L. Sievenpiper
john.sievenpiper@utoronto.ca
Full list of author information is available at the end of the article© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to theoriginal author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images orother third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line
to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory
regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of thislicence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecom‑mons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Erlich et al. BMC Medicine(2024) 22:336

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difference, − 0.26 mmol/L [95% confidence interval, − 0.43 to − 0.10]), systolic blood pressure (− 8.00 mmHg [− 14.89
to − 1.11]), and diastolic blood pressure (− 4.74 mmHg [− 9.17 to − 0.31]); small important reductions in LDL-C(− 0.19 mmol/L [− 0.29 to − 0.09]) and c-reactive protein (CRP) (− 0.82 mg/L [− 1.26 to − 0.37]); and trivial increases
in HDL-C (0.05 mmol/L [0.00 to 0.09]). No other outcomes showed differences. There was no meaningful effect modi‑
fication by added sugars across outcomes. The certainty of evidence was high for LDL-C and non-HDL-C; moderate
for systolic blood pressure, diastolic blood pressure, CRP, and HDL-C; and generally moderate-to-low for all otheroutcomes. We could not conduct the sub-study of the effect of lactose versus added sugars, as no eligible trials could
be identified.
Conclusions Current evidence provides a good indication that replacing cow’s milk with soymilk (including sweet‑
ened soymilk) does not adversely affect established cardiometabolic risk factors and may result in advantages
for blood lipids, blood pressure, and inflammation in adults with a mix of health statuses. The classification of plantbased dairy alternatives such as soymilk as ultra-processed may be misleading as it relates to their cardiometabolic
effects and may need to be reconsidered in the transition to plant-based diets.
Trial registration ClinicalTrials.gov identifier, NCT05637866.
Keywords Soy protein, Soymilk, Cardiovascular disease, Systematic review, Meta-analysis, Randomized controlled
feeding trials

Background
Major dietary guidelines recommend a shift to plantbased diets for public and planetary health [1–8], while
recommending simultaneous reductions in ultra-processed foods [2–8]. The shift to plant-based diets has
resulted in an explosion of dairy, meat, and egg alternatives with plant protein foods projected to reach almost
10% of the global protein market by 2030 [9]. Althoughthese foods can aid in the transition to plant-based
diets, food classification systems such as the World
Health Organization (WHO)-endorsed NOVA classification system classify them as ultra-processed foods to
be avoided [10].
Dairy alternatives are an important example of a foodcategory at the crossroads of these competing recommendations. School milk programs provide > 150 million
servings of cow’s milk to children worldwide [11]. These
programs are in addition to the food service and procurement policies of public institutions such as schools, universities, hospitals, long-term care homes, and prisons.
Many of these programs and policies do not allow for
the free replacement of cow’s milk with nutrient-dense
plant milks [12, 13]. Although the Dietary Guidelines
for Americans [1], Canada’s Food Guide [3], and several
European food-based dietary guidelines [14] recognize
fortified soymilk [1] as nutritionally equivalent to cow’s
milk, school nutrition programs in the United States (US)[12] and Europe [13] only provide funding for cow’s milk.
There is a bipartisan bill before the US congress to change
this policy and provide funding for fortified soymilk [15].
A major barrier to the use of fortified soymilk is that it
contains added sugars to match the sweetness of cow’s
milk at a level which would disqualify it from meeting
the Food and Drug Administration’s proposed definitionof “healthy” [16] (although its total sugar content is usually ~ 60% less than that of cow’s milk given the higher
sweetness intensity of sucrose vs lactose) [17] and isclassified (irrespective of its sugar content) as an ultraprocessed food to be avoided [10, 18]. Cow’s milk, on theother hand, enjoys classification as a “healthy,” minimallyprocessed food to be encouraged [10, 18].
As industry innovates in response to the growing
demand and policy makers develop public health nutrition policies and programs in response to the evolving dietary guidance for more plant-based diets, it isimportant to understand whether nutrient-dense ultraprocessed plant protein foods can replace minimallyprocessed dairy foods without the adverse cardiometabolic effects attributed to added sugars and ultra-processed foods. We conducted a systematic review andmeta-analysis of randomized controlled trials of theeffect of substituting soymilk for minimally processed
cow’s milk and its modification by added sugars (sweetened versus unsweetened) on intermediate cardiometabolic outcomes as a basis for understanding the role ofnutrient-dense ultra-processed plant protein foods in thetransition to plant-based diets.

Methods
Design

We followed the Cochrane Handbook for Systematic
Reviews of Interventions to conduct this systematic
review and meta-analysis and reported our results by
the Preferred Reporting Items for Systematic Reviews
and Meta-Analysis (PRISMA) guidelines [19, 20] (Additional file 1: Table 1). To explore whether added sugars mediate any effects observed in sweetened soymilk
studies, we conducted an additional systematic review

Erlich et al. BMC Medicine(2024) 22:336

and meta-analysis sub-study. This separate investigation followed the same protocol and methodology as
our main study. It focused on controlled trials examining the impact of lactose in isocaloric comparisons withfructose-containing sugars (such as sucrose, high-fructose corn syrup [HFCS], or fructose) when not included
in a dairy-like matrix, on all outcomes in the main
study. The protocol is registered at ClinicalTrials.gov(NCT05637866).
Data sources and search strategy

We searched MEDLINE, Embase, and the Cochrane Central Register of Controlled Trials databases through June
2024. The detailed search strategies for the main study
and sub-study were based on validated search terms [21](Additional file 1: Tables 2 and 4). Manual searches of thereference lists of included studies supplemented the systematic search.
Study selection

The main study included randomized controlled trials inhuman adults with any health status. Included trials had
a study duration of ≥ 3 weeks and investigated the effects
of soymilk compared with cow’s milk in energy matched
conditions on intermediate cardiometabolic outcomes(Additional file 1: Table 3). Trials that included othercomparators that were not cow’s milk or had no viable
outcome data were excluded. No restrictions were placedon language. For the sub-study, we included controlled
trials involving adults of all health statuses that had astudy duration of ≥ 3 weeks and investigated the effects ofadded sugars compared with lactose on the same intermediate cardiometabolic outcomes (Additional file 1:
Table 5).
Data extraction

A minimum of two investigators (ME, DG, SBM, AA)
independently extracted relevant data from eligible studies. Extracted data included study design, sample size,
sample characteristics (age, body mass index [BMI],
sex, health status), intervention characteristics (soymilk
volume, total sugars content, soy protein dose), control
characteristics (cow’s milk volume, total sugars content,
milk protein dose, milk fat content), baseline outcome
levels, background diet, follow-up duration, setting,funding sources, and outcome data. The authors were
contacted for missing outcome data when it was indicated that a relevant outcome was measured but notreported. Graphically presented data were extracted fromfigures using Plot Digitizer [22].

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Outcomes

Outcomes for the main study and sub-study included
blood lipids (low-density lipoprotein cholesterol [LDLC], high-density lipoprotein cholesterol [HDL-C],
non-high-density lipoprotein cholesterol [non-HDLC], triglycerides, and apolipoprotein B [ApoB]), glycemic control (hemoglobin A1c [HbA1c], fasting plasma
glucose, 2-h postprandial glucose, fasting insulin, andplasma glucose area under the curve [PG-AUC]), bloodpressure (systolic blood pressure and diastolic bloodpressure), inflammation (c-reactive protein [CRP]),
adiposity (body weight, BMI, body fat, and waist circumference), kidney function and structure (creatinine,creatinine clearance, glomerular filtration rate [GFR],
estimated glomerular filtration rate [eGFR], albuminuria, and albumin-creatinine ratio [ACR]), uric acid, andnon-alcoholic fatty liver disease (NAFLD) (intrahepatocellular lipid [IHCL], alanine transaminase [ALT],
aspartate aminotransferase [AST], and fatty liver
index).
Mean differences (MDs) between the intervention
and control arm and respective standard errors wereextracted for each trial. If these were not provided, they
were derived from available data using published formulas [19]. Mean pairwise difference in change-frombaseline values were preferred over end values. When
median data was provided, they were converted tomean data with corresponding variances using methods
developed by McGrath et al. [23]. When no variancedata was available, the standard deviation of the MDs
was borrowed from a trial similar in size, participants,
and nature of intervention. All disagreements were reconciled by consensus or with a senior reviewer (JLS).
Risk of bias assessment

Included studies were assessed for the risk of bias independently and in duplicate by at least two investigators (ME, DG, SBM, AA) using the Cochrane Risk ofBias (ROB) 2 Tool [24]. The assessment was performed
across six domains of bias (randomization process,deviations from intended interventions, missing outcome data, measurement of the outcome, selection of
the reported result, and overall bias). Crossover studies
were assessed for an additional domain of bias (risk ofbias arising from period or carryover effects). The ROB
for each domain was assessed as “low” (plausible bias
unlikely to seriously alter the results), “high” (plausible bias that seriously weakens confidence in results),
or “some concern” (plausible bias that raises somedoubt about the results). Reviewer discrepancies wereresolved by consensus or arbitration by a senior investigator (JLS).

Erlich et al. BMC Medicine(2024) 22:336

Statistical analysis

STATA (version 17; StataCorp LP, College Station,TX) was used for all analyses for the main study andsub-study. The principal effect measures were themean pair-wise differences in change from baseline(or alternatively, end differences) between the intervention arm providing the soymilk and the cow’s milk
comparator/control arm in each trial (significanceat PMD < 0.05). Results are reported as MDs with 95%
confidence intervals (95% CI). As one of our primaryresearch questions relates to the role of added sugars
as a mediator in any observed differences between
soymilk and cow’s milk, we stratified results by thepresence of added sugars in the soymilk (sweetened
versus unsweetened) and assessed effect modification by this variable on pooled estimates. Data werepooled using the generic inverse variance method with
DerSimonian and Laird random effect models [25].
Fixed effects were used when less than five trials wereavailable for an outcome [26]. A paired analysis wasapplied for crossover designs and for within-individual correlation coefficient between treatment of 0.5 as
described by Elbourne et al. [27, 28].
Heterogeneity was assessed using the Cochran’s Q
statistic and quantified using the I2 statistic, whereI2 ≥ 50% and PQ < 0.10 were used as evidence of substantial heterogeneity [19]. Potential sources of heterogeneity were explored using sensitivity analyses.
Sensitivity analyses were done via two methods. We
conducted an influence analysis by systematicallyremoving one trial at a time and recalculating theoverall effect estimate and heterogeneity. A trial was
considered influential if its removal explained the substantial heterogeneity or altered the direction, magnitude, or significance of the summary estimate. To
determine whether the overall summary estimates
were robust to the use of an assumed correlation coefficient for crossover trials, we conducted a second
sensitivity analysis by using correlation coefficients of
0.25 and 0.75. If ≥ 10 trials were available, meta-regression analyses were used to assess the significance ofeach subgroup categorically and when possible, continuously (significance at P < 0.05). A priori subgroup
analyses included soy protein dose, follow-up duration, baseline outcome levels, comparator, design, age,
health status, funding, and risk of bias.
If ≥ 6 trials are available [29], dose–response analyses were performed using meta-regression to assesslinear (by generalized least squares trend (GLST) estimation models) and non-linear spline curve modeling (by
MKSPLINE procedure) dose–response gradients (significance at P < 0.05).

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If ≥ 10 studies were available, publication bias wasassessed by inspection of contour-enhanced funnel plots
and formal testing with Egger’s and Begg’s tests (significance at P < 0.10) [30–32]. If evidence of publication
bias was suspected, the Duval and Tweedie trim-and-fillmethod was performed to adjust for funnel plot asymmetry by imputing missing study data and assess for smallstudy effects [33].
Certainty of evidence

The Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach was used
to assess the certainty of evidence. The GRADE Handbook and GRADEpro V.3.2 software were used [34,35]. A minimum of two investigators (ME, DG, SBM)
independently performed GRADE assessments foreach outcome [36]. Discrepancies were resolved byconsensus or arbitration by the senior author (JLS).
The overall certainty of evidence was graded as either
high, moderate, low, or very low. Randomized trials
are initially graded as high by default and then downgraded or upgraded based on prespecified criteria.
Reasons for downgrading the evidence included study
limitations (risk of bias assessed by the Cochrane ROB
Tool), inconsistency of results (substantial unexplainedinterstudy heterogeneity, I2 > 50% and PQ < 0.10), indirectness of evidence (presence of factors that limit thegeneralizability of the results), imprecision (the 95% CI
for effect estimates overlap with the MID for benefit orharm), and publication bias (evidence of small-study
effects). The evidence was upgraded if a significant
dose–response gradient was detected. We defined
the importance of the magnitude of the pooled effectestimates using prespecified MIDs (Additional file 1:
Table 6) with GRADE guidance [36–50] according
to five levels: very large (≥ 10 MID); large (≥ 5 MID);
moderate (≥ 2 MID); small important (≥ 1 MID); andtrivial/unimportant (< 1 MID) effects.

Results
Search results

Figure 1 in Appendix shows the flow of the literature for the main analysis. We identified 522 reports
through database and manual searches. A total of 17
reports [51–67] met the inclusion criteria and contained data for LDL (10 trials, n = 312), HDL-C (8 trials,
n = 271), non-HDL-C (7 trials, n = 243), triglycerides (9trials, n = 278), HbA1c (1 trial, n = 25), fasting plasma
glucose (5 trials, n = 147), 2-h plasma glucose (1 trial,n = 28), fasting insulin (4 trials, n = 119), systolic bloodpressure (5 trials, n = 158), diastolic blood pressure (5Erlich et al. BMC Medicine(2024) 22:336

trials, n = 158), CRP (5 trials, n = 147), body weight (6trials, n = 163), BMI (6 trials, n = 173), body fat (1 trial,n = 43), waist circumference (3 trials, n = 90), creatinine (1 trial, n = 25), eGFR (1 trial, n = 25), ALT (1 trial,n = 24), and AST (1 trial, n = 24) involving 504 participants. No trials were available for ApoB, PG-AUC, creatinine clearance, eGFR, albuminuria, ACR, uric acid,
IHCL, or fatty liver index.
Additional file 1: Fig. 1 shows the flow of literature forthe sub-study. We identified 1010 reports through database and manual searches. After excluding 305 duplicates, a total of 705 reports were reviewed by title andabstract. No reports met the inclusion criteria and therefore no data was available for analysis.
Trial characteristics

Table 1 shows the characteristics of the included trials.
The trials were conducted in a variety of locations, withmost conducted in Iran (7/17 trials, 41%), followed by
the US (3/17 trials, 18%), Italy (2/17 trials, 12%), Brazil (1/17 trials, 6%), Scotland (1/17 trials, 6%), Sweden(1/17 trials, 6%), Spain (1/17 trials, 6%), and Australia(1/17 trials, 6%). All trials took place in outpatient settings (17/17, 100%). The median trial size was 25 participants (range, 7–60 participants). The median age ofthe participants was 48.5 years (range, 20–70 years) andthe median BMI was 27.9 kg/m2 (range, 20–31.1 kg/
m2). The trials included participants with hypercholesterolemia (4/17 trials, 25%), overweight or obesity (4/17
trials, 25%), type 2 diabetes (2/17 trials, 12%), hypertension (1/17 trials, 6%), rheumatoid arthritis (1/17
trials, 6%), or were healthy (3/17 trials, 18%) or postmenopausal (2/17 trials, 12%). Both trials with crossover design (10/17 trials, 59%) and parallel design (7/17
trials, 41%) were included. The intervention included
sweetened (11/17 trials, 65%) and unsweetened (6/17
trials, 35%) soymilk.
The median soymilk dose was 500 mL/day (range,
240–1000 mL/day) with a median soy protein of 22 g/
day (range, 2.5–70 g/day) or 6.6 g/250 mL (range,
2.6–35 g/250 mL) and median total (added) sugarsof 17.2 g/day (range, 4.0–32 g/day) or 6.9 g/250 mL(range, 1–16 g/250 mL) in the sweetened soymilk. The
comparators included skim (0% milk fat) (2/17 trials,12%), low-fat (1% milk fat) (4/17 trials, 24%), reducedfat (1.5–2.5% milk fat) (7/17 trials, 41%), and whole(3% milk fat) (1/17 trials, 6%) cow’s milk. Three trials
did not report the milk fat content of cow’s milk used.
The median cow’s milk dose was 500 mL/day (range,
236–1000 mL/day) with a median milk protein of 24 g/

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day (range, 3.3–70 g/day) or 8.3 g/250 mL (range, 3.4–
35 g/250 mL) and median total (lactose) sugars of 24 g/
day (range, 11.5–49.2 g/day) or 12 g/250 mL (range,
10.8–12.8 g/250 mL). The median study duration was
4 weeks (range, 4–16 weeks). The trials received funding from industry (1/17 trials, 6%), agency (8/17 trials, 47%), both industry and agency (4/16 trials, 25%),or they did not report the funding source (4/17 trials,
24%).
Risk of bias assessment

Additional file 1: Fig. 2 shows the ROB assessments of
the included trials. Two trials were assessed as havingsome concerns from period or carryover effects: Bricarello et al. [53] and Steele [67]. All other trials were
judged as having an overall low risk of bias. There was
no evidence of serious risk of bias across the included
trials.
Outcomes
Markers of blood lipids

Figure 2 and Additional file 1: Figs. 3–6 show the effectof substituting soymilk for cow’s milk on markers ofblood lipids. The substitution resulted in a small important reduction in LDL-C (10 trials; MD: − 0.19 mmol/L;
95% CI: − 0.29 to − 0.09 mmol/L; PMD < 0.001; no heterogeneity: I2 = 0.0%; PQ = 0.823), a trivial increase in
HDL-C (8 trials; MD: 0.05 mmol/L; 95% CI: 0.00 to
0.09 mmol/L; PMD = 0.036; no heterogeneity: I2 = 0.0%;
PQ = 0.053), a moderate reduction in non-HDL-C (7 trials; MD: − 0.26 mmol/L; 95% CI: − 0.43 to − 0.10 mmol/L;
PMD = 0.002; no heterogeneity: I2 = 0.0%; PQ = 0.977), andno effect on triglycerides. There were no interactions
by added sugars in soymilk for any blood lipid markers(P = 0.49–0.821).
Markers of glycemic control

Figure 2 and Additional file 1: Figs. 7–10 show the effectof substituting soymilk for cow’s milk on markers of glycemic control. The substitution had no effect on HbA1c,
fasting plasma glucose, 2-h plasma glucose, or fasting
insulin. There was no interaction by added sugars insoymilk for fasting plasma glucose (P = 0.747) but there
was an interaction for fasting insulin (P = 0.026), where alack of effect remained in both groups with neither the
sweetened soymilk (non-significant increasing effect)
nor the unsweetened soymilk (non-significant decreasing
effect) showing an effect on fasting insulin. We could notassess this interaction for HbA1c or 2-h plasma glucose,as there was only one trial available for each outcome.

Erlich et al. BMC Medicine(2024) 22:336

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Figure 2 and Additional file 1: Figs. 14–17 show the effectof substituting soymilk for cow’s milk on markers of adiposity. The substitution had no effect on body weight,
BMI, body fat, or waist circumference. There were nointeractions by added sugars in soymilk for any adiposity
outcome (P = 0.664–0.733).

[53] or Steele [67] each resulted in loss of significant
effect for HDL-C. The removal of Onning et al. [62] or
Steele [67] each resulted in a partial explanation of heterogeneity for triglycerides. The removal of Hasanpour
et al. [56] explained the heterogeneity for fasting insulin.
The removal of Keshavarz et al. [57] or Miraghajani et al.
[59] each resulted in a loss of significant effect for systolic blood pressure and the removal of Rivas et al. [63]resulted in a partial explanation of the heterogeneity forsystolic blood pressure. The removal of Hasanpour et al.
[56], Keshavarz et al. [57], Miraghajani et al. [59], or Rivas
et al. [63] each resulted in a loss of significant effect fordiastolic blood pressure and the removal of Rivas et al.
[63] resulted in a partial explanation of heterogeneity fordiastolic blood pressure. The removal of MohammadShahi et al. [58] resulted in loss of significant effect for
CRP.
Additional file 1: Table 8 shows the sensitivity analyses
for the different correlation coefficients (0.25 and 0.75)
used in paired analyses of crossover trials for all outcomes. The different correlation coefficients did not alter
the direction, magnitude, or significance of the effect orevidence for heterogeneity, with the following exceptions: loss of significance for the effect of the substitution
on HDL-C (8 trials; MD: 0.04 mmol/L; 95% CI: − 0.10 to
0.01 mmol/L; PMD = 0.107; I2 = 0.0%; PQ = 0.670) with theuse of 0.25 and (8 trials; MD: 0.05 mmol/L; 95% CI: − 0.10
to 0.01 mmol/L; PMD = 0.089; I2 = 0.0%; PQ = 0.640) withthe use of 0.75.

Markers of kidney function

Subgroup analyses

Figure 2 and Additional file 1: Figs. 18 and 19 show theeffect of substituting soymilk for cow’s milk on markers of kidney function. The substitution had no effect on
creatinine or eGFR. We could not assess the interaction
by added sugars in soymilk for creatinine or eGFR, as
there was only one trial available for each outcome which
included soymilk without added sugars.

Additional file 1: Figs. 34–36 present the subgroup analyses and continuous meta-regression analyses for LDLC. Subgroup analysis was not conducted for any otheroutcome as there were < 10 trials included. There wasno significant effect modification by health status, BMI,
age, comparator, baseline LDL-C, study design, followup duration, funding source, dose of soy protein, orrisk of bias for LDL-C. However, there were tendencies
towards a greater reduction in LDL-C by point estimatesin groups with certain health statuses (hypercholesterolemic and overweight/obesity), a higher baseline LDLC, and a higher soy protein dose (> 25 g/day).

Blood pressure

Figure 2 and Additional file 1: Figs. 11 and 12 show theeffect of substituting soymilk for cow’s milk on blood pressure. The substitution resulted in a moderate reduction inboth systolic blood pressure (5 trials; MD: − 8.00 mmHg;
95% CI: − 14.89 to − 1.11 mmHg; PMD = 0.023; substantial heterogeneity: I2 = 86.89%; PQ ≤ 0.001) and diastolicblood pressure (5 trials; MD: − 4.74 mmHg; 95% CI: − 9.17
to − 0.31 mmHg; PMD = 0.036; substantial heterogeneity: I2 = 77.3%; PQ = 0.001). There were no interactions byadded sugars in soymilk for blood pressure (P = 0.747 and
0.964).
Markers of inflammation

Figure 2 and Additional file 1: Fig. 13 show the effect of
substituting soymilk for cow’s milk on markers of inflammation. The substitution resulted in a small important reduction in CRP (5 trials; MD: − 0.81 mg/dL; 95%
CI: − 1.26 to − 0.37 mg/dL; PMD = < 0.001; no heterogeneity: I2 = 0.0%; PQ = 0.814). There was no interaction byadded sugars in soymilk for CRP (P = 0.275).
Markers of adiposity

Markers of NAFLD

Figure 2 and Additional file 1: Figs. 20 and 21 show theeffect of substituting soymilk for cow’s milk on markers of NAFLD. The substitution had no effect on ALT or
AST. We could not assess heterogeneity or the interaction by added sugars in soymilk for ALT or AST, as there
was only one trial available for each outcome which
included soymilk without added sugars.
Sensitivity analysis

Additional file 1: Figs. 22–33 present the influence analyses across all outcomes. The removal of Bricarello et al.

Dose–response analyses

Additional file 1: Figs. 37–42 present linear and nonlinear dose–response analyses for LDL-C, HDL-C,
non-HDL-C, triglycerides, body weight, and BMI.
There was no dose–response seen for the effect of substituting soymilk for cow’s milk, with the exceptionof a positive linear dose–response for triglycerides

Erlich et al. BMC Medicine(2024) 22:336(Plinear = 0.038). We did not downgrade the certainty ofevidence as the greater reduction in triglycerides seen
at lower doses of soy protein was lost at higher doses.
There were no dose–response analyses performed forthe remaining outcomes because there were < 6 trials
available for each.
Publication bias assessment

Additional file 1: Fig. 43 presents the contour-enhanced
funnel plot for assessment of publication bias for LDLC. There was no asymmetry at the visual inspection andno evidence (Begg’s test = 0.721, Egger’s test = 0.856) offunnel plot asymmetry for LDL-C. No other publication
bias analyses could be performed as there were < 10 trials
available for each.
Adverse events and acceptability

Additional file 1: Table 9 shows the reported adverse
events and acceptability of study beverages. Adverse
events were reported in nine trials. In one trial byGardner et al. [55], one participant experienced arecurrence of a cancer; however, it was considered to
be unrelated to the short-term consumption of thestudy milks. Three trials (Miraghajani et al., Hasanpour et al., and Mohammad-Shahi, et al.) [56, 58, 59]
reported one to two withdrawals due to digestive difficulties related to soymilk consumption. Two trials(Sirtori et al. 1999 and 2002) [65, 66] reported one ormore participants with digestive difficulties related tocow’s milk consumption. Two trials (Nourieh et al. and
Keshavarz et al.) [57, 61] each reported two participant
withdrawals related to digestive problems that were
not specific to either study beverage. Of these, four trials indicated that most participants found the soymilk
and cow’s milk acceptable and tolerable. One trial, byOnning et al. [62], incorporated a sensory evaluationof appearance, consistency, flavor, and overall impression, which showed declining scores for both types ofmilk over the 3-week test period.
GRADE assessment

Additional file 1: Table 10 presents the GRADE assessment. The certainty of evidence for the effect of substituting soymilk for cow’s milk was high for LDL-C,
non-HDL-C, fasting plasma glucose, and waist circumference. The certainty of evidence was moderate for
HDL-C, triglycerides, fasting insulin, systolic blood pressure, diastolic blood pressure, CRP, body weight, and
BMI owing to a downgrade for imprecision of the pooled
effect estimates and was moderate for body fat owing toa downgrade for indirectness. The certainty of evidence
was low for HbA1c, 2-h plasma glucose, creatinine,

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eGFR, ALT, and AST owing to downgrades for indirectness and imprecision.

Discussion
We conducted a systematic review and meta-analysisof 17 trials that examined the effect of substituting
soymilk (median dose of 22 g/day or 6.6 g/250 mL
serving of soy protein per day and 17.2 g/day or
6.9 g/250 mL of total [added] sugars in the sweetened soymilk) for cow’s milk (median dose of 24 g/
day or 8.3 g/250 mL of milk protein and 24 g/day or
12 g/250 mL of total sugars [lactose]) and its modification by added sugars (sweetened versus unsweetened soymilk) on 19 intermediate cardiometabolic
outcomes over a median follow-up period of 4 weeks
in adults of varying health status. The substitutionof soymilk for cows’ milk led to moderate reductions in non-HDL-C (− 0.26 mmol/L or ~ − 7%) andsystolic blood pressure (− 8.00 mmHg) and diastolic blood pressure (− 4.74 mmHg); small important
reductions in LDL-C (− 0.19 mmol/L or ~ − 6%) andCRP (− 0.81 mg/L or ~ 22%); and a trivial increase in
HDL-C (0.05 mmol/L or ~ 4%), with no adverse effects
on other intermediate cardiometabolic outcomes.
There was no meaningful interaction by added sugars
in soymilk, with sweetened and unsweetened soymilk
showing similar effects across outcomes. There was nodose–response relationship seen across the outcomesfor which dose–response analyses were performed.
Findings in relation to the literature

Our findings agree with previous evidence synthesesof soy. Regulatory authorities such as the United States
Food and Drug Administration (FDA) and Health
Canada have conducted comprehensive evaluationsof the randomized controlled trials of the effect of soyprotein from different sources on total-C and LDLC, resulting in approved health claims for soy protein(based on an intake of 25 g/day of soy protein irrespective of source) for cholesterol reduction [68] andcoronary heart disease risk reduction [69]. Updated
systematic reviews and meta-analyses of the 46 randomized controlled trials included in the re-evaluationof the FDA health claim [70] showed reductions in
LDL-C of − 3.2% [71]. This reduction has been stablesince the health claim was first approved in 1999 [72]and is smaller but consistent with our findings specifically for soymilk. No increase in HDL-C, however, wasdetected. Previous systematic reviews and meta-analyses of randomized controlled trials of soy protein andsoy isoflavones have also shown significant but smaller
reductions in systolic blood pressure (1.70 mmHg)

Erlich et al. BMC Medicine(2024) 22:336

and diastolic blood pressure (− 1.27 mmHg) [73] than
was found in the current analysis. These reductionsin LDL-C and blood pressure are further supported
by reductions in clinical events with updated pooled
analyses of prospective cohort studies showing thatlegumes including soy are associated with reduced
incidence of total cardiovascular disease and coronary
heart disease [74].
Systematic reviews and meta-analyses that specifically isolated the effect of soymilk (as a single foodmatrix) in its intended substitution for cow’s milk arelacking. Sohouli and coworkers [75] conducted a systematic review and meta-analysis of 18 randomized
controlled trials in 665 individuals of varying healthstatus that assessed the effect of soymilk in comparison with a mix of comparators on intermediate
cardiometabolic outcomes but did not isolate its substitution with cow’s milk. This synthesis showed similar improvements in LDL-C (− 0.24 mmol/L), systolicblood pressure (− 7.38 mmHg), diastolic blood pressure(− 4.36 mmHg), and CRP (− 1.07, mg/L), while also
showing reductions in waist circumference and TNF-α[75]. The substitution of legumes that includes soy forvarious animal protein sources and more specificallylegumes/nuts (the only exposure available) for dairy insyntheses of prospective cohort studies has also shown
reductions in incident total cardiovascular disease andall-cause mortality [76].
Indirect evidence from dietary patterns that containsoy foods including soymilk in substitution for different
animal sources of protein including cow’s milk further
supports our findings. Systematic reviews and metaanalyses of randomized trials of the Portfolio diet andvegetarian and vegan dietary patterns have shown additive reductions in LDL-C, non-HDL-C, blood pressure,and CRP when soy foods including soymilk are combinedwith other foods that target these same intermediate risk
factors with displacement of different animal sources ofprotein including cow’s milk [77, 78]. These reductions
have also been shown to translate to reductions in clinicalevents with systematic reviews and meta-analyses of prospective cohort studies showing that adherence to these
dietary patterns is associated with reductions in incident
coronary heart disease, total cardiovascular disease, andall-cause mortality [79–81].
Potential mechanisms of action

The potential mechanism mediating the effects of soy
remains unclear. Specific components within the soy
food matrix, including soy protein and phytochemicals like isoflavones [82], have been implicated. The

Page 8 of 16

well-established lipid-lowering effect of soy [72] may
be attributed to the 7S globulin fraction of soy protein,which exerts its primary action by upregulating LDL-C
receptors predominantly within the liver, thereby augmenting the clearance of LDL-C from circulation [82].
The isoflavone, fiber, fatty acids, and anti-nutrient
components may also exert some mediation [83]. The
reduction in blood pressure has been most linked tothe soy isoflavones [83]. There is evidence that soy isoflavones may modulate the renin–angiotensin–aldosterone system (RAAS), with the capacity to inhibit the
production of angiotensin II and aldosterone, thereby
contributing to the regulation of blood pressure[73]. Another blood pressure lowering mechanism
may involve the ability of soy isoflavones to enhanceendothelial function by mitigating oxidative stress andinflammation, consequently promoting the release of
the relaxing factor nitric oxide (NO) [73]. This potential mechanism of isoflavones may also explain the
reductions seen in inflammation.
Strengths and limitations

Our evidence synthesis had several strengths. First,
we completed a comprehensive and reproducible systematic search and selection process of the available
literature examining the effect of substituting soymilk
for cow’s milk on intermediate cardiometabolic outcomes. Second, we synthesized the totality of available
evidence from a large body of randomized controlled
trials, which gives the greatest protection against systematic error. Third, we included an extensive andcomprehensive list of outcomes to fully capture theimpact of soymilk on cardiometabolic health. Fourth,
we only included randomized controlled trials thatcompared soymilk to cow’s milk directly, to increase
the specificity of our conclusion. Finally, we included
a GRADE assessment to explore the certainty of available evidence.
There were also several limitations. First, we could
not conduct the sub-study of the effect of lactose versus added sugars outside of a dairy-like matrix, as no
eligible trials could be identified. Although this analysis is important for isolating the effect of added sugars as a mediator of any adverse effects, we did notobserve any meaningful interaction by added sugars insoymilk. Second, there was serious imprecision in thepooled estimates across many of the outcomes with
the 95% confidence intervals overlapping the MID ineach case, with the exception of LDL-C, non-HDLC, fasting plasma glucose, and waist circumference.
The certainty of evidence for HDL-C, triglycerides,

Erlich et al. BMC Medicine(2024) 22:336

HbA1c, fasting plasma glucose, 2-h plasma glucose,fasting insulin, systolic blood pressure, diastolic bloodpressure, CRP, body weight, BMI, body fat, creatinine,eGFR, ALT, and AST was downgraded for this reason. Third, there was evidence of indirectness related
to insufficient trials for HbA1c, 2-h plasma glucose,creatinine, eGFR, ALT, and AST, which limits generalizability. Each outcome with data from only 1 trial
was downgraded for this reason. Another source of
indirectness could be the median follow-up duration of 4 weeks (range, 4–16 weeks). This time frame
may be sufficient for observing certain effects, butother outcomes may require a longer period to manifest changes. Despite acknowledging this variation in
response time among different outcomes, we did notfurther downgrade for this aspect of indirectness.
Instead, we tailored our conclusions to reflect shortto-moderate term effects. Finally, although publication
bias was not suspected, we were only able to make thisassessment for LDL-C, as there were < 10 trials for allother outcomes.
Considering these strengths and limitations, we
assessed the certainty of evidence as high for LDL-C andnon-HDL-C; moderate for systolic blood pressure, diastolic blood pressure, CRP, and HDL-C; and moderate-tolow for all outcomes where significant effects were notobserved.
Implications

This work has important implications for plant protein foods in the recommended shift to more plantbased diets. Major international dietary guidelines in
the US [1], Canada [3], and Europe [4–6] recommend
fortified soymilk as the only suitable replacement for
cow’s milk. Our findings support this recommendation showing soymilk including sweetened soymilk(up to 7 g added sugars per 250 mL) does not haveany adverse effects compared with cow’s milk across
19 intermediate cardiometabolic outcomes with benefits for lipids, blood pressure, and inflammation.
This evidence suggests that it may be misleading as it
relates to their cardiometabolic effects to classify fortified soymilk as an ultra-processed food to be avoided
while classifying cow’s milk as a minimally processedfood to be encouraged (based on the WHO-endorsed
NOVA classification system [10]). It also suggests thatit may be misleading not to allow fortified soymilkthat is sweetened with small amounts of sugars to beclassified as “healthy” (based on the FDA’s new proposed definition that only permits this claim on products with added sugars ≤ 2.5 g or 5% daily value (DV)Page 9 of 16

per 250 mL serving [16]). The proposed FDA criteria
would prevent this claim on soymilk products designed
to be iso-sweet analogs of cow’s milk (in which 5 g or
10% daily value [DV] of added sugars from sucrose insoymilk is equivalent to the 12 g of lactose in cow’s
milk per 250 mL serving, as sucrose is 1.4 sweeter thanlactose [17]). To prevent confusion, policy makers may
want to exempt fortified soymilk from classification
as an ultra-processed food and allow added sugars up
to 10% DV for the definition of “healthy,” as has been
proposed by the FDA for sodium and saturated fat indairy products (including soy-based dairy alternatives) to account for accepted processing and preservation methods [16]. These policy considerations would
balance the need to limit nutrient-poor energy-dense
foods with the need to promote nutrient-dense foods
like fortified soymilk in the shift to healthy plant-based
diets.

Conclusions
In conclusion, the evidence provides a good indication that substituting either sweetened or unsweetenedsoymilk for cow’s milk in adults with varying healthstatuses does not have the adverse effects on intermediate cardiometabolic outcomes attributed to addedsugars and ultra-processed foods in the short-to-moderate term. There appear even to be advantages withsmall to moderate reductions in established markers ofblood lipids (LDL-C, non-HDL-C) that are in line withapproved health claims for cholesterol and coronary
heart disease risk reduction, as well as small to moderate reductions in blood pressure and inflammation(CRP). Sources of uncertainty include imprecision andindirectness in several of the estimates. There remains
a need for more well-powered randomized controlled
trials of the effect of substituting soymilk for cow’s
milk on less studied intermediate cardiometabolic outcomes, especially established markers of glycemic control, kidney structure and function, and NAFLD. Thereis also a need for trials comparing lactose versus addedsugars outside of a dairy-like matrix to understandbetter the role of added sugars at different levels in
substitution for lactose across outcomes. In the meantime, our findings support the use of fortified soymilk
with up to 7 g added sugars per 250 mL as a suitablereplacement for cow’s milk and suggest that its classification as ultra-processed and/or not healthy based on
small amounts of added sugars may be misleading andneed to be reconsidered to facilitate the recommended
transition to plant-based diets.

Erlich et al. BMC Medicine(2024) 22:336

Page 10 of 16

Appendix

Fig. 1 Flow of literature on the effect of substituting soymilk for cow’s milk on intermediate cardiometabolic outcomes. Exclusion criteria: duplicate,
abstract only (conference abstract), non-human (animal study), in vitro, review/position paper/commentary/letter, observational (observational study),
no soymilk (intervention was not soymilk), children (participants < 18 years of age), no suitable comparator (comparator was not cow’s milk), isolated
soy protein (an ISP powder was given to participants), acute (follow-up of < 3 weeks), combined intervention (effects of intervention and comparator
could not be isolated), wrong endpoint (no data for outcomes of interest), alternative publication (repeated data from original publication)

Erlich et al. BMC Medicine(2024) 22:336

Page 11 of 16

Fig. 2 A summary plot for the effect of substituting soymilk for cow’s milk on intermediate cardiometabolic outcomes. Analyses were conducted using
generic, inverse variance random-effects models (at least 5 trials available), or fixed-effects models (fewer than 5 trials available). Between-study
heterogeneity was assessed by the Cochrane Q statistic, where PQ < 0.100 was considered statistically significant, and quantified by the I2 statistic,
where I2 ≥ 50% was considered evidence of substantial heterogeneity. The GRADE of randomized controlled trials are rated as “high” certainty of evidence
and can be downgraded by 5 domains and upgraded by 1 domain. The white squares represent no downgrades, the filled black squares indicate a single
downgrade or upgrades for each outcome, and the black square with a white “2” indicates a double downgrade for each outcome. Because all included
trials were randomized or nonrandomized controlled trials, the certainty of the evidence was graded as high for all outcomes by default and thendowngraded or upgraded based on prespecified criteria. Criteria for downgrades included risk of bias (downgraded if most trials were considered to beat high ROB); inconsistency (downgraded if there was substantial unexplained heterogeneity: I2 ≥ 50%; PQ < 0.10); indirectness (downgraded if there werefactors absent or present relating to the participants, interventions, or outcomes that limited the generalizability of the results); imprecision (downgraded
if the 95% CI crossed the minimally important difference (MID) for harm or benefit); and publication bias (downgraded if there was evidence of publica‑tion bias based on the funnel plot asymmetry and/or significant Egger or Begg test (P < 0.10)), with confirmation by adjustment using the trim-and-fill
analysis of Duval and Tweedie. The criteria for upgrades included a significant dose–response gradient. For the interpretation of the magnitude, we used
the MIDs to assess the importance of magnitude of our point estimate using the effect size categories according to the new GRADE guidance. Then, weused the MIDs to assess the importance of the magnitude of our point estimates using the effect size categories according to the GRADE guidanceas follows: a large effect (≥ 5 × MID); moderate effect (≥ 2 × MID); small important effect (≥ 1 × MID); and trivial/unimportant effect (< 1 MID). *HDL-C values
reversed to show benefit. **LDL-C was not downgraded for imprecision, as the degree to which the upper 95% CI crosses the MID is not clinicallymeaningful. Additionally, the moderate change in non-HDL-C, with high certainty of evidence, substantiates the high certainty of the LDL-C results.

Erlich et al. BMC Medicine(2024) 22:336

Page 12 of 16

Table 1 Summary characteristics for the main analysis
Total (17 trials), N (range) or
median (range)

Sweetened soymilk (11 trials), N(range) or median (range)

Unsweetened soymilk (6 trials), N(range) or median (range)

Participants

25 (7–60)

24 (21–40)

34 (7–60)

Health statusa

4 HC, 4 OW/OB, 3 healthy, 2 T2D, 2
PM, 1 HTN, 1 RA

3 HC, 2 OW/OB, 2 healthy, 2 PM, 1
HTN, 1 RA

1 HC, 2 OW/OB, 1 healthy, 2 T2DSex

392 females, 112 males

229 females, 70 males

163 females, 42 males

Age, years

48.5 (20–70)

46 (18–70)

44.5 (20–56)

BMI, kg/m2

27.9 (20–31.1)

26 (22.5–31.1)

28 (24.9–30.9)

Settingb

17 OP

11 OP

6 OP

Setting; country

7 Iran, 3 US, 2 Italy, 1 Brazil, 1 Scot‑
land, 1 Sweden, 1 Spain, 1 Australia

3 Iran, 3 US, 2 Italy, 1 Sweden, 1
Spain, 1 Australia

4 Iran, 1 Brazil, 1 Scotland

Design10 control, 7 parallel

7 control, 4 parallel

3 control, 3 parallel

Background diet12 energy neutral, 3 energy
reduced, 2 protein limited

7 habitual, 2 energy reduced, 2protein limited

5 energy neutral, 1 energy reduced

Follow-up, weeks

4 (4–16)

4 (4–16)

5 (4–8)Funding sources

1 industry, 8 agency, 4 agency
and industry, 4 undisclosed

1 industry, 4 agency, 2 agency
and industry, 4 undisclosed

4 agency, 2 agency and industry

Soymilk
Added sugars

11 yes, 6 no

11 yes

6 noVolume, mL

500 (240–1000)

500 (240–1000)450 (240–1000)

Total sugars, g/day

17.2 (4–32)

19.2 (4–32)

4 (0–5)

Total sugars, g/250 mL

6.9 (1–16)

8 (1–16)

1.3 (0–3.6)

Protein, g/day

22 (2.5–70)

24 (2.5–70)

14 (2.5–37)

Protein, g/250 mL

6.6 (2.6–35)

7.5 (2.6–35)

6.3 (2.5–37)

Cow’s milk
Added sugars

17 no

0 yes

17 no

Volume, mL

500 (236–1000)

500 (240–1000)450 (240–1000)

Total sugars, g/day

24 (11.5–49.2)

24 (11.9–49.2)

21.6 (11.5–48)

Total sugars, g/250 mL

12 (10.8–12.8)

12 (11.8–12.8)

12 (10.8–12.3)

Protein, g/day

24 (3.3–70)

24 (3.3–70)

15 (3.3–35)

Protein, g/250 mL

8.3 (3.4–35)

8.5 (3.4–35)

8.3 (3.4–8.3)

Fat contentc

2 skim, 3 low fat, 8 reduced fat, 1
whole, 3 undisclosed

1 skim, 2 low fat, 5 reduced fat, 1
whole, 2 undisclosed

1 skim, 1 low fat, 3 reduced fat, 1
undiscloseda

OW/OB, overweight or obesity; PM, post-menopausal; HC, hypercholesterolemic; RA, rheumatoid arthritis; T2D, type 2 diabetic; HTN, hypertensive. bOP, outpatient.
Skim: 0% milk fat, low fat: 1% milk fat, reduced fat: 1.5–2.5% milk fat, whole: 3% milk fatcAbbreviations
GRADE	Grading of Recommendations, Assessment, Development, andEvaluation
non-HDL-C	Non-high-density lipoprotein cholesterol
LDL-C	Low-density lipoprotein cholesterol
CRP	C-reactive protein
HDL-C	High-density lipoprotein cholesterolWHO	World Health Organization
US	United States
PRISMA	
Preferred Reporting Items for Systematic Reviews andMeta-Analysis
HFCS	High-fructose corn syrup
BMI	Body mass index
ApoB	Apolipoprotein B
HbA1c	Hemoglobin A1c
PG-AUC​	Plasma glucose area under the curve
GFR	Glomerular filtration rateeGFR	Estimated glomerular filtration rate
ACR​	Albumin-creatinine ratio
NAFLD	Non-alcoholic fatty liver disease
IHCL	Intrahepatocellular lipid

ALT	Alanine transaminase
AST	Aspartate aminotransferase
MD	Mean difference
ROB	Risk of bias
95% CI	95% Confidence interval
GLST	Generalized least squares trend
FDA	Food and Drug Administration
TNF-a	Tumor necrosis factor alpha
RAAS	Renin-angiotensin-aldosterone system
NO	Nitric oxide
DV	Daily value

Supplementary Information
The online version contains supplementary material available at https://​doi.​
org/​10.​1186/​s12916-​024-​03524-7.
Additional file 1: This file contains Additional file 1 material, including the
PRISMA checklist, further details on the search process, and additional
results.

Erlich et al. BMC Medicine(2024) 22:336

Acknowledgements
Aspects of this work were presented at the following conferences: Canadian
Nutrition Society (CNS), Quebec City, Canada, May 4–6, 2023; 40th Interna‑
tional Symposium on Diabetes and Nutrition, Pula, Croatia, June 15–18, 2023;
and Nutrition 2023—American Society for Nutrition (ASN), Boston, USA, July
22–25, 2023.
Authors’ Twitter handles@Toronto_3D_Unit.
Authors’ contributions
The authors’ responsibilities were as follows: JLS designed the research(conception, development of overall research plan, and study oversight); ME
and DG acquired the data; ME, SBM, TAK, and SAC performed the data analysis;
JLS, ME, DG, SBM, AA, TAK, and LC interpreted the data; JLS and ME drafted
the manuscript, have primary responsibility for the final content, and takeresponsibility for the integrity of the data and accuracy of the data analysis;
JLS, MNE, DG, SBM, TAK, LC, AZ, SAC, AA, MM, LAL, RPB, CWCK, and DJD con‑tributed to the project conception and critical revision of the manuscript forimportant intellectual content and read and approved the final version of themanuscript. The corresponding author attests that all listed authors meet theauthorship criteria and that no others meeting the criteria have been omitted.
All authors read and approved the final manuscript.
Funding
This work was supported by the United Soybean Board (the United States
Department of Agriculture Soybean Checkoff Program [funding reference
number, 2411–108-0101]) and the Canadian Institutes of Health Research(funding reference number, 129920) through the Canada-wide Human Nutri‑tion Trialists’ Network (NTN). The Diet, Digestive tract, and Disease (3D) Centre,
funded through the Canada Foundation for Innovation and the Ministry of
Research and Innovation’s Ontario Research Fund, provided the infrastructure
for the conduct of this work. ME was funded by a CIHR Canada Graduate
Scholarship and Toronto 3D PhD Scholarship award. DG was funded by an
Ontario Graduate Scholarship. TAK and AZ were funded by a Toronto 3D Post‑
doctoral Fellowship Award. LC was funded by a Toronto 3D New InvestigatorAward. SA-C was funded by a CIHR Canadian Graduate Scholarship. DJAJ was
funded by the Government of Canada through the Canada Research Chair
Endowment. None of the sponsors had any role in study design; in the collec‑
tion, analysis, and interpretation of data; in the writing of the report; and in thedecision to submit the article for publication. But one of the co-authors, Mark
Messina, who was involved in all aspects of the study except data collection or
analysis, is the Director of Nutrition Science and Research at the Soy Nutrition
Institute Global, an organization that receives partial funding from the princi‑
pal funder, the United Soybean Board (USB).
Availability of data and materials
All data generated or analyzed during this study are included in this publishedarticle and its Additional file 1: information files.

Declarations
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
TAK reports receiving grants from Institute for the Advancement of Food andNutrition Sciences (IAFNS, formerly ILSI North America) and National Honey
Board (USDA Checkoff program). He has received honorariums from
Advancement of Food and Nutrition Sciences (IAFNS), the International Food
Information Council (IFIC), the Calorie Control Council (CCC), the International
Sweeteners Association (ISA), and AmCham Dubai. He has received funding
from the Toronto 3D Knowledge Synthesis and Clinical Trials foundation. LC
has received research support from the Canadian Institutes of health Research(CIHR), Protein Industries Canada (a Government of Canada Global Innovation
Clusters), The United Soybean Board (USDA soy “Checkoff” program), and theAlberta Pulse Growers Association. AZ is a part-time research associate atPage 13 of 16

INQUIS Clinical Research, Ltd., a contract research organization. She hasreceived consulting fees from Glycemic Index Foundation Inc. SA-C has
received an honorarium from the International Food Information Council (IFIC)
for a talk on artificial sweeteners, the gut microbiome, and the risk fordiabetes. MM was employed by the Soy Nutrition Institute Global, an
organization that receives funding from the United Soybean Board (USB) andfrom members involved in the soy industry. RPB has received industrial grants,
including those matched by the Canadian government, and/or travel support
or consulting fees largely related to work on brain fatty acid metabolism or
nutrition from Arctic Nutrition, Bunge Ltd., Dairy Farmers of Canada, DSM,
Fonterra Inc, Mead Johnson, Natures Crops International, Nestec Inc.
Pharmavite, Sancero Inc., and Spore Wellness Inc. Moreover, Dr. Bazinet is on
the executive of the International Society for the Study of Fatty Acids and
Lipids and held a meeting on behalf of Fatty Acids and Cell Signaling, both ofwhich rely on corporate sponsorship. Dr. Bazinet has given expert testimony inrelation to supplements and the brain. DJAJ has received research grants
from Saskatchewan & Alberta Pulse Growers Associations, the Agricultural
Bioproducts Innovation Program through the Pulse Research Network, theAdvanced Foods and Material Network, Loblaw Companies Ltd., Unilever
Canada and Netherlands, Barilla, the Almond Board of California, Agricultureand Agri-food Canada, Pulse Canada, Kellogg’s Company, Canada, Quaker
Oats, Canada, Procter & Gamble Technical Centre Ltd., Bayer Consumer Care,
Springfield, NJ, Pepsi/Quaker, International Nut & Dried Fruit Council (INC), Soy
Foods Association of North America, the Coca-Cola Company (investigatorinitiated, unrestricted grant), Solae, Haine Celestial, the Sanitarium Company,
Orafti, the International Tree Nut Council Nutrition Research and Education
Foundation, the Peanut Institute, Soy Nutrition Institute (SNI), the Canola andFlax Councils of Canada, the Calorie Control Council, the Canadian Institutes of
Health Research (CIHR), the Canada Foundation for Innovation (CFI), and theOntario Research Fund (ORF). He has received in-kind supplies for trials as a
research support from the Almond board of California, Walnut Council of
California, the Peanut Institute, Barilla, Unilever, Unico, Primo, Loblaw
Companies, Quaker (Pepsico), Pristine Gourmet, Bunge Limited, Kellogg
Canada, and WhiteWave Foods. He has been on the speaker’s panel, served on
the scientific advisory board and/or received travel support and/or honoraria
from Lawson Centre Nutrition Digital Series, Nutritional Fundamentals for
Health (NFH)-Nutramedica, Saint Barnabas Medical Center, The University of
Chicago, 2020 China Glycemic Index (GI) International Conference, Atlantic
Pain Conference, Academy of Life Long Learning, the Almond Board of
California, Canadian Agriculture Policy Institute, Loblaw Companies Ltd, theGriffin Hospital (for the development of the NuVal scoring system), theCoca-Cola Company, Epicure, Danone, Diet Quality Photo Navigation (DQPN),
Better Therapeutics (FareWell), Verywell, True Health Initiative (THI), Heali AI
Corp, Institute of Food Technologists (IFT), Soy Nutrition Institute (SNI),
Herbalife Nutrition Institute (HNI), Saskatchewan & Alberta Pulse Growers
Associations, Sanitarium Company, Orafti, the International Tree Nut Council
Nutrition Research and Education Foundation, the Peanut Institute, Herbalife
International, Pacific Health Laboratories, Barilla, Metagenics, Bayer Consumer
Care, Unilever Canada and Netherlands, Solae, Kellogg, Quaker Oats, Procter &Gamble, Abbott Laboratories, Dean Foods, the California StrawberryCommission, Haine Celestial, PepsiCo, the Alpro Foundation, Pioneer Hi-Bred
International, DuPont Nutrition and Health, Spherix Consulting andWhiteWave Foods, the Advanced Foods and Material Network, the Canola andFlax Councils of Canada, Agri-Culture and Agri-Food Canada, the CanadianAgri-Food Policy Institute, Pulse Canada, the Soy Foods Association of North
America, the Nutrition Foundation of Italy (NFI), Nutra-Source Diagnostics, theMcDougall Program, the Toronto Knowledge Translation Group (St. Michael’s
Hospital), the Canadian College of Naturopathic Medicine, The Hospital for
Sick Children, the Canadian Nutrition Society (CNS), the American Society of
Nutrition (ASN), Arizona State University, Paolo Sorbini Foundation, and theInstitute of Nutrition, Metabolism and Diabetes. He received an honorariumfrom the United States Department of Agriculture to present the 2013 W.O.
Atwater Memorial Lecture. He received the 2013 Award for Excellence in
Research from the International Nut and Dried Fruit Council. He received
funding and travel support from the Canadian Society of Endocrinology andMetabolism to produce mini cases for the Canadian Diabetes Association(CDA). He is a member of the International Carbohydrate Quality Consortium(ICQC). His wife, Alexandra L Jenkins, is a director and partner of INQUIS
Clinical Research for the Food Industry, his 2 daughters, Wendy Jenkins andAmy Jenkins, have published a vegetarian book that promotes the use of thefoods described here, The Portfolio Diet for Cardiovascular Risk ReductionErlich et al. BMC Medicine(2024) 22:336(Academic Press/Elsevier 2020 ISBN:978–0-12–810510-8), and his sister,Caroline Brydson, received funding through a grant from the St. Michael’s
Hospital Foundation to develop a cookbook for one of his studies. He is also avegan. CWCK has received grants or research support from the Advanced
Food Materials Network, Agriculture and Agri-Foods Canada (AAFC), Almond
Board of California, Barilla, Canadian Institutes of Health Research (CIHR),
Canola Council of Canada, International Nut and Dried Fruit Council,
International Tree Nut Council Research and Education Foundation, Loblaw
Brands Ltd, the Peanut Institute, Pulse Canada, and Unilever. He has received
in-kind research support from the Almond Board of California, Barilla, California
Walnut Commission, Kellogg Canada, Loblaw Companies, Nutrartis, Quaker(PepsiCo), the Peanut Institute, Primo, Unico, Unilever, and WhiteWave Foods/
Danone. He has received travel support and/or honoraria from the Barilla,
California Walnut Commission, Canola Council of Canada, General Mills,
International Nut and Dried Fruit Council, International Pasta Organization,
Lantmannen, Loblaw Brands Ltd, Nutrition Foundation of Italy, Oldways
Preservation Trust, Paramount Farms, the Peanut Institute, Pulse Canada,
Sun-Maid, Tate & Lyle, Unilever, and White Wave Foods/Danone. He has servedon the scientific advisory board for the International Tree Nut Council,
International Pasta Organization, McCormick Science Institute, and Oldways
Preservation Trust. He is a founding member of the International Carbohydrate
Quality Consortium (ICQC), Executive Board Member of the Diabetes andNutrition Study Group (DNSG) of the European Association for the Study ofDiabetes (EASD), is on the Clinical Practice Guidelines Expert Committee for
Nutrition Therapy of the EASD, and is a Director of the Toronto 3D Knowledge
Synthesis and Clinical Trials foundation. JLS has received research support
from the Canadian Foundation for Innovation, Ontario Research Fund,
Province of Ontario Ministry of Research and Innovation and Science,
Canadian Institutes of health Research (CIHR), Diabetes Canada, American
Society for Nutrition (ASN), National Honey Board (U.S. Department ofAgriculture [USDA] honey “Checkoff” program), Institute for the Advancementof Food and Nutrition Sciences (IAFNS), Pulse Canada, Quaker Oats Center of
Excellence, INC International Nut and Dried Fruit Council Foundation, The
United Soybean Board (USDA soy “Checkoff” program), Protein Industries
Canada (a Government of Canada Global Innovation Cluster), Almond Boardof California, European Fruit Juice Association, The Tate and Lyle Nutritional
Research Fund at the University of Toronto, The Glycemic Control andCardiovascular Disease in Type 2 Diabetes Fund at the University of Toronto (afund established by the Alberta Pulse Growers), The Plant Protein Fund at the
University of Toronto (a fund which has received contributions from IFF
among other donors), The Plant Milk Fund at the University of Toronto (a fundestablished by the Karuna Foundation through Vegan Grants), and The
Nutrition Trialists Network Fund at the University of Toronto (a fundestablished by donations from the Calorie Control Council and PhysiciansCommittee for Responsible Medicine). He has received food donations to
support randomized controlled trials from the Almond Board of California,
California Walnut Commission, Danone, Nutrartis, Soylent, and Dairy Farmers
of Canada. He has received travel support, speaker fees and/or honoraria from
Danone, FoodMinds LLC, Nestlé, Abbott, General Mills, Nutrition Communica‑
tions, International Food Information Council (IFIC), Arab Beverages,
International Sweeteners Association, Association Calorie Control Council, andPhynova. He has or has had ad hoc consulting arrangements with Perkins Coie
LLP, Tate & Lyle, Ingredion, and Brightseed. He is on the Clinical Practice
Guidelines Expert Committees of Diabetes Canada, European Association forthe study of Diabetes (EASD), Canadian Cardiovascular Society (CCS), andObesity Canada/Canadian Association of Bariatric Physicians and Surgeons.
He serves as an unpaid member of the Board of Trustees of IAFNS. He is a
Director at Large of the Canadian Nutrition Society (CNS), founding memberof the International Carbohydrate Quality Consortium (ICQC), Executive Board
Member of the Diabetes and Nutrition Study Group (DNSG) of the EASD, andDirector of the Toronto 3D Knowledge Synthesis and Clinical Trials foundation.
His spouse is an employee of AB InBev. All other authors declare that they
have no competing interests.
Author details
1
Department of Nutritional Sciences, Temerty Faculty of Medicine, Univer‑sity of Toronto, Toronto, ON, Canada. 2 Toronto 3D Knowledge Synthesis
and Clinical Trials Unit, Clinical Nutrition and Risk Factor Modification Centre,
St. Michael’s Hospital, Toronto, ON, Canada. 3 Li Ka Shing Knowledge Institute,
St. Michael’s Hospital, Toronto, ON, Canada. 4 Royal College of Surgeonsin Ireland, Dublin, Ireland. 5 Soy Nutrition Institute Global, Washington, DC,

Page 14 of 16

USA. 6 Department of Medicine, Temerty Faculty of Medicine, Universityof Toronto, Toronto, ON, Canada. 7 Division of Endocrinology and Metabolism,
Department of Medicine, St. Michael’s Hospital, Toronto, ON, Canada. 8 Collegeof Pharmacy and Nutrition, University of Saskatchewan, Saskatoon, SK, Canada.
Received: 20 December 2023 Accepted: 9 July 2024

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